Adding Debugging Scenario tests for V1 APIs

test/Microsoft.ML.Functional.Tests/Debugging.cs

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+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using System.Collections.Generic;
+using Microsoft.ML.Data;
+using Microsoft.ML.Functional.Tests.Datasets;
+using Microsoft.ML.RunTests;
+using Microsoft.ML.TestFramework;
+using Microsoft.ML.Trainers;
+using Microsoft.ML.Transforms.Text;
+using Xunit;
+using Xunit.Abstractions;
+
+namespace Microsoft.ML.Functional.Tests
+{
+    public class Debugging : BaseTestClass
+    {
+        public Debugging(ITestOutputHelper output) : base(output)
+        {
+        }
+
+        /// <summary>
+        /// Debugging: The individual pipeline steps can be inspected to see what is happening to 
+        /// data as it flows through.
+        /// </summary>
+        /// <remarks>
+        /// It should, possibly through the debugger, be not such a pain to actually
+        /// see what is happening to your data when you apply this or that transform. For example, if I
+        /// were to have the text "Help I'm a bug!" I should be able to see the steps where it is
+        /// normalized to "help i'm a bug" then tokenized into ["help", "i'm", "a", "bug"] then
+        /// mapped into term numbers [203, 25, 3, 511] then projected into the sparse
+        /// float vector {3:1, 25:1, 203:1, 511:1}, etc. etc.
+        /// </remarks>
+        [Fact]
+        void InspectIntermediatePipelineSteps()
+        {
+            var mlContext = new MLContext(seed: 1);
+
+            var data = mlContext.Data.LoadFromEnumerable<TweetSentiment>(
+                new TweetSentiment[]
+                {
+                    new TweetSentiment { Sentiment = true, SentimentText = "I love ML.NET." },
+                    new TweetSentiment { Sentiment = true, SentimentText = "I love TLC." },
+                    new TweetSentiment { Sentiment = false, SentimentText = "I dislike fika." }
+                });
+
+            // create a training pipeline.
+            var pipeline = mlContext.Transforms.Text.FeaturizeText(
+                "Features",
+                new TextFeaturizingEstimator.Options
+                {
+                    KeepPunctuations = false,
+                    OutputTokens = true,
+                    CharFeatureExtractor = null, // new WordBagEstimator.Options { NgramLength = 0, SkipLength = -1 },
+                    WordFeatureExtractor = new WordBagEstimator.Options { NgramLength = 1},
+                    Norm = TextFeaturizingEstimator.NormFunction.None
+                },
+                "SentimentText");
+
+            // Fit the pipeline to the data.
+            var model = pipeline.Fit(data);
+
+            // Transform the data.
+            var transformedData = model.Transform(data);
+
+            var preview = transformedData.Preview();
+
+            // Verify that columns can be inspected.
+            // Validate the tokens column.
+            var tokensColumn = transformedData.GetColumn<string[]>(transformedData.Schema["Features_TransformedText"]);
+            var expectedTokens = new string[3][]
+            {
+                new string[] {"i", "love", "mlnet"},
+                new string[] {"i", "love", "tlc"},
+                new string[] {"i", "dislike", "fika"},
+            };
+            int i = 0;
+            foreach (var rowTokens in tokensColumn)
+                Assert.Equal(expectedTokens[i++], rowTokens);
+
+            // Validate the Features column.
+            var featuresColumn = transformedData.GetColumn<float[]>(transformedData.Schema["Features"]);
+            var expectedFeatures = new float[3][]
+            {
+                new float[6] { 1, 1, 1, 0, 0 ,0 },
+                new float[6] { 1, 1, 0, 1, 0, 0 },
+                new float[6] { 1, 0, 0, 0, 1, 1 }
+            };
+            i = 0;
+            foreach (var rowFeatures in featuresColumn)
+                Assert.Equal(expectedFeatures[i++], rowFeatures);
+        }
+
+        /// <summary>
+        /// Debugging: The schema of the pipeline can be inspected.
+        /// </summary>
+        [Fact]
+        public void InspectPipelineSchema()
+        {
+            var mlContext = new MLContext(seed: 1);
+
+            // Get the dataset.
+            var data = mlContext.Data.LoadFromTextFile<HousingRegression>(GetDataPath(TestDatasets.housing.trainFilename), hasHeader: true);
+
+            // Define a pipeline
+            var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
+                .Append(mlContext.Transforms.Normalize())
+                .AppendCacheCheckpoint(mlContext)
+                .Append(mlContext.Regression.Trainers.Sdca(
+                    new SdcaRegressionTrainer.Options { NumberOfThreads = 1, MaximumNumberOfIterations = 20 }));
+
+            // Fit the pipeline to the data.
+            var model = pipeline.Fit(data);
+
+            // Inspect the model schema, and verify that a Score column is produced.
+            var outputSchema = model.GetOutputSchema(data.Schema);
+            var columnNames = new string[outputSchema.Count];
+            int i = 0;
+            foreach (var column in outputSchema)
+                columnNames[i++] = column.Name;
+            Assert.Contains("Score", columnNames);
+        }
+
+        /// <summary>
+        /// Debugging: The schema read in can be verified by inspecting the data.
+        /// </summary>
+        [Fact]
+        public void InspectSchemaUponLoadingData()
+        {
+            var mlContext = new MLContext(seed: 1);
+
+            // Get the dataset.
+            var data = mlContext.Data.LoadFromTextFile<HousingRegression>(GetDataPath(TestDatasets.housing.trainFilename), hasHeader: true);
+
+            // Verify the column names.
+            int i = 0;
+            foreach (var column in data.Schema)
+            {
+                if (i == 0)
+                    Assert.Equal("Label", column.Name);
+                else
+                    Assert.Equal(HousingRegression.Features[i-1], column.Name);
+                i++;
+            }
+
+            // Verify that I can cast it to the right schema by inspecting the first row.
+            foreach (var row in mlContext.Data.CreateEnumerable<HousingRegression>(mlContext.Data.TakeRows(data, 1), true))
+            {
+                // Validate there was data in the row by checking that some values were not zero since zero is the default.
+                var rowSum = row.MedianHomeValue;
+                foreach (var property in HousingRegression.Features)
+                    rowSum += (float) row.GetType().GetProperty(property).GetValue(row, null);
+
+                Assert.NotEqual(0, rowSum);
+            }
+        }
+
+        /// <summary>
+        /// Debugging: The progress of training can be accessed.
+        /// </summary>
+        [Fact]
+        public void ViewTrainingOutput()
+        {
+            var mlContext = new MLContext(seed: 1);
+
+            // Attach a listener.
+            var logWatcher = new LogWatcher();
+            mlContext.Log += logWatcher.ObserveEvent;
+
+            // Get the dataset.
+            var data = mlContext.Data.LoadFromTextFile<HousingRegression>(GetDataPath(TestDatasets.housing.trainFilename), hasHeader: true);
+
+            // Define a pipeline
+            var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
+                .Append(mlContext.Transforms.Normalize())
+                .AppendCacheCheckpoint(mlContext)
+                .Append(mlContext.Regression.Trainers.Sdca(
+                    new SdcaRegressionTrainer.Options { NumberOfThreads = 1, MaximumNumberOfIterations = 20 }));
+
+            // Fit the pipeline to the data.
+            var model = pipeline.Fit(data);
+
+            // Validate that we can read lines from the file.
+            var expectedLines = new string[3] {
+                @"[Source=SdcaTrainerBase; Training, Kind=Info] Auto-tuning parameters: L2 = 0.001.",
+                @"[Source=SdcaTrainerBase; Training, Kind=Info] Auto-tuning parameters: L1Threshold (L1/L2) = 0.",
+                @"[Source=SdcaTrainerBase; Training, Kind=Info] Using best model from iteration 7."};
+            foreach (var line in expectedLines)
+            {
+                Assert.Contains(line, logWatcher.Lines);
+                Assert.Equal(1, logWatcher.Lines[line]);
+            }
+        }
+
+        internal class LogWatcher {
+
+            public readonly IDictionary<string, int> Lines;
+
+            public LogWatcher()
+            {
+                Lines = new Dictionary<string, int>();
+            }
+
+            public void ObserveEvent(object sender, LoggingEventArgs e)
+            {
+                if (Lines.ContainsKey(e.Message))
+                    Lines[e.Message]++;
+                else
+                    Lines[e.Message] = 1;
+            }
+        }
+    }
+}